<p>The “local” and “global” weights are calculated for each aggregated attribute separately and are distributed in 3 levels of aggregation. Bold and non-bold terms represent basic attributes and aggregated terms, respectively.</p
<p>Aggregate size fractions and mean weight diameter (MWD) under different treatments.</p
<p>The form of the relationships used to rescale the values of each landscape feature to between zer...
<p>Aggregation rules for the 18 possible combinations of the 3 cultivar choices, the 2 levels of fer...
<p>Bolded and non-bold terms represent aggregated and basic attributes, respectively.</p
<p>All the scales are ordered from values detrimental to the crop (i.e. favourable to eyespot) on th...
<p>The attribute weighting models and the numbers of important protein features selected by each mod...
Variable weights calculated for each explanatory variable in the full models for abundance.</p
<p>The numbers and the averages of most important alleles (fragments) selected by different attribut...
<p>Main features of the datasets used for the evaluation of IPSIM-Wheat-Eyespot's predictive quality...
<p>Relationships between grain weight per spikelet, grain number, and single-grain weight at differe...
<p>Weighted Means/Proportions of Dependent and Independent Variables Used in the Models (N = 2,108),...
<p>This table presents the number of algorithms that selected the attribute. Weighting algorithms we...
Variable weights calculated for each explanatory variable in the full model for bee richness, commun...
<p>Feature weights of (a) <i>P</i><sub>1</sub>' and (b) <i>P</i><sub>2</sub>': attribute values with...
<p>Column weights obtained from SVM rank values (a), and (b). These are the total weight percentages...
<p>Aggregate size fractions and mean weight diameter (MWD) under different treatments.</p
<p>The form of the relationships used to rescale the values of each landscape feature to between zer...
<p>Aggregation rules for the 18 possible combinations of the 3 cultivar choices, the 2 levels of fer...
<p>Bolded and non-bold terms represent aggregated and basic attributes, respectively.</p
<p>All the scales are ordered from values detrimental to the crop (i.e. favourable to eyespot) on th...
<p>The attribute weighting models and the numbers of important protein features selected by each mod...
Variable weights calculated for each explanatory variable in the full models for abundance.</p
<p>The numbers and the averages of most important alleles (fragments) selected by different attribut...
<p>Main features of the datasets used for the evaluation of IPSIM-Wheat-Eyespot's predictive quality...
<p>Relationships between grain weight per spikelet, grain number, and single-grain weight at differe...
<p>Weighted Means/Proportions of Dependent and Independent Variables Used in the Models (N = 2,108),...
<p>This table presents the number of algorithms that selected the attribute. Weighting algorithms we...
Variable weights calculated for each explanatory variable in the full model for bee richness, commun...
<p>Feature weights of (a) <i>P</i><sub>1</sub>' and (b) <i>P</i><sub>2</sub>': attribute values with...
<p>Column weights obtained from SVM rank values (a), and (b). These are the total weight percentages...
<p>Aggregate size fractions and mean weight diameter (MWD) under different treatments.</p
<p>The form of the relationships used to rescale the values of each landscape feature to between zer...
<p>Aggregation rules for the 18 possible combinations of the 3 cultivar choices, the 2 levels of fer...